System and a method for observing and predicting a physiological state of an animal

ABSTRACT

A system for observing and predicting a physiological state of an animal has been provided. The system includes a computer including a processor and being operatively connected to a database, at least one sample providing device for repetitively providing at least one sample of a body fluid of the animal, an analysis apparatus for analysing the at least one sample, so as to obtain at least one sample value of at least one parameter of the body fluid, a data interface for repetitively entering the sample value of the at least one parameter in the database, where the database is adapted to store multiple database entries representing the sample value of the at least one parameter at various points in time, and where the processor is programmed to perform at least one mathematical analysis of the at least one sample value, and selecting, on the basis of the at least one mathematical analysis, the point in time for providing a subsequent sample and performing a subsequent analysis of the subsequent sample for at least one of the parameters.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional patentapplications 60/403,645, filed Aug. 16, 2002 and 60/408,286, filed Sep.6, 2002, the contents of which are incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to systems and methods for observing andpredicting a physiological state of an animal. As an example, the animalmay be a cow. The systems and methods of the invention rely on a sampleof a body fluid of the animal, such as urine, blood or milk.

BACKGROUND OF THE INVENTION

It is generally desirable to observe, watch and predict thephysiological state of an animal, in particular a farm animal. Thus,body fluids of animals, in particular milk, urine and blood may beanalysed in order to obtain values of parameters, such as cell count inmilk, lactate dehydrogenase (LDH), N-Acetyl-β-D-glucosaminidase(NAGase), ketone bodies such as acetoacetate, beta-hydroxybutyrate (BHB)and acetone, urea content, progesterone, or others, each of which byitself or in combination with others indicates a certain physiologicalstate. For example, a high LDH concentration usually indicates mastitis,whereas the progesterone content may indicate a pregnancy state.

The prior art has proposed various methods for analysis of milk samples.For example, U.S. Pat. No. 5,252,829 discloses a method for determiningthe amount of urea in milk. In a milk sample, the absorption of infraredradiation in various wavelength intervals are determined, whereby urea,fat, lactose, and protein absorb in different wavelength intervals.

U.S. Pat. No. 6,405,672 and de Mol (2000) discloses a system and amethod for monitoring the physical condition of a herd of livestock,errors between values predicted in accordance with a time-series modeland corresponding measured values are used for determining a confidenceinterval for a prediction for each animal individually the significanceof an error between a prediction and a measured value regarding thelikelihood that the animal is in heat or suffers from a disease isautomatically assessed for each animal individually.

SUMMARY OF THE INVENTION

With the aim of providing an improved tool for observing and predictinga physiological state of an animal, a first aspect of the presentinvention provides a system for observing and predicting a physiologicalstate of an animal, the system comprising:

-   -   a computer comprising a processor and being operatively        connected to a database,    -   at least one sample providing device for repetitively providing        at least one sample of a body fluid of the animal,    -   an analysis apparatus for analysing the at least one sample, so        as to obtain at least one sample value of at least one parameter        of the body fluid,    -   a data interface for repetitively entering the sample value of        the at least one parameter in the database,        wherein the database is adapted to store multiple database        entries representing the sample value of the at least one        parameter at various points in time, and wherein the processor        is programmed to:    -   perform at least one mathematical analysis of the at least one        sample value, and    -   selecting, on the basis of the at least one mathematical        analysis, the point in time for providing a subsequent sample        and performing a subsequent analysis of said subsequent sample        for at least one of the parameters.

In a second aspect, the present invention provides a method forobserving and predicting a physiological state of an animal, the methodcomprising:

-   -   repetitively providing at least one sample of a body fluid of        the animal,    -   analysing the at least one sample, so as to obtain at least one        sample value of at least one parameter of the body fluid,    -   entering the sample value of the at least one of parameter in a        database of a computer system,        whereby the database is loaded with multiple database entries        representing the sample value of the at least one parameter at        various points in time, and    -   performing at least one mathematical analysis of the at least        one sample value, and    -   selecting, on the basis of the at least one mathematical        analysis, the point in time for providing a subsequent sample        and performing a subsequent analysis of said subsequent sample        for at least one of the parameters.

In the mentioned systems and methods the mathematical analysis ispreferably a statistical method.

The statistical analysis is preferably selected from the groupconsisting of a univariate analysis of the database entries to obtain afirst set of data representing expected sample values of at least one ofthe parameters at future points in time or a multivariate analysis ofthe database entries to produce a second set of data derived fromcombined analysis of sample values of at least two parameters. The firstand second sets of data can be combined to obtain a third set of datarepresentative of the physiological state of the animal and the obtainedfirst, second and third sets of data may be stored in the database.

The types of univariate analysis and multivariate analysis useable inthe present invention are disclosed by the non-limiting examplesmentioned later in the specification.

In order to improve the measurement of at least one parameter forobserving and predicting a physiological state of an animal the databasecomprises at least one external value of at least one externalparameter. The external parameter is disclosed later in thespecification.

In an embodiment of the present invention at least one externalparameter is included in the database, such as at least 2 externalparameters, e.g. at least 3 external parameters, such as at least 4external parameters, e.g. at least 5 external parameters, such as atleast 6 external parameters, e.g. at least 10 external parameters, suchas at least 15 external parameters, e.g. at least 25 externalparameters, such as at least 50 external parameters.

The system is programmed to and the method further comprises the step ofperforming data analysis of the database entries to obtain an indicationof the physiological state of the animal, whereby the external value isincluded in the data analysis.

It is highly advantageous that an ideal production performancemonitoring system and a method is capable of generating quantitativeanalytical data for selected parameters for which even relatively smallday-to-day variations are highly predictive for a change in e.g. theoverall health condition, the physiological condition, nutritional andenergy state, the state in the oestrus cycle or pregnancy of theindividual population member being tested. This requires that the systemprovided permits frequent quantitative analyses to be made at acost-effective level.

Furthermore, it is an advantageous feature of the invention that theparameters can be analysed in a dynamic and intelligent mode, i.e. thatonly those parameters which, at a given point in time of thereproduction and/or lactation cycle of the individual population membersshould be analysed in a particular milk sample. This is achieved byproviding a computer system for storing data including data for thephysiological and nutritional state of said each population individualmember including data indicating point in time in the reproduction andlactation cycles. An analysis apparatus for analysing a plurality ofparameters in a sample is generating a detectable signal in the presenceof an individual sample parameter. The determination of whether aparameter shall be analysed or not is being controlled by the computerhaving data stored for the physiological and nutritional state of eachindividual population member such that the analysis is only activated atselected points in time or at selected time intervals. In thisconnection, one interesting feature is that the computer having datastored for the physiological and nutritional state of each individualpopulation member is continuously updated with new data, so that theselection of the range of parameters that are analysed in a given sampleat a given point in time is based on a constantly updated set of datafor the particular population member.

The system and the method may select, on the basis of the at least onemathematical analysis, the point in time for providing a subsequentsample and performing analysis of said subsequent sample for at leastone of the parameters. This selection is e.g. provided by as soon as thesample value of a selected parameter differ by more than a givendeviation measure X, from the expected sample value the sample frequencywill be increased for measuring the selected parameter. In the situationwhere the sample value of the selected parameter is not differing bymore than the given measure X from the expected sample value, the samplefrequency may be decreased for measuring the selected parameter. Thedeviation measure X may be a relative measure or an absolute measure.The aforementioned deviation measure may easily be determined inaccordance with mathematical and statistical methods well known to aperson skilled in the art.

In another aspect, the invention provides a system for observing andpredicting a physiological state of an animal, the system comprising:

-   -   a computer comprising a processor and being operatively        connected to a database,    -   at least one sample providing device for repetitively providing        at least one sample of a body fluid of the animal,    -   an analysis apparatus for analysing the at least one sample, so        as to obtain at least one sample value of each of a plurality of        parameters of the body fluid,    -   a data interface for repetitively entering the sample value of        each of the parameters in the database,        wherein the database is adapted to store multiple database        entries representing the sample value of each of the parameters        at various points in time, and wherein the processor is        programmed to:    -   perform a univariate analysis of the database entries to obtain        a first set of data representing expected sample values of at        least one of the parameters at future points in time,    -   perform a multivariate analysis of the database entries to        produce a second set of data derived from combined analysis of        sample values of at least two of the parameters,    -   combine the first and second sets of data to obtain a third set        of data representative of the physiological state of the animal,        and    -   store the first, second and third sets of data in the database.

The benefits of univariate and multivariate data analysis are utilisedin order to more precisely predict or observe the physiological state bytaking into account a plurality of parameters. Thus, an accurateanalysis is made, which results in an indication of a current state,with a view to make it possible to predict future states. Embodiments ofthe system of the invention may be arranged close to the animals, forexample in a milking parlour of a farm, and they may be operable by afarmer or a farm technician. Accordingly, initial indications of thephysiological state of, e.g. cows in a herd, may be provided to a farmerwithout the farmer having to involve a veterinarian in the initialassessment of the physiological state of, e.g. a cow.

In the present context, the term “physiological state” should beunderstood as a state in a general sense. For example, it may be a statewith respect to health, including state with respect to clinical orsubclinical diseases, reproduction, or energy status.

In the present context, the term “population” refers to a relevant groupof animals, e.g. a particular herd, a particular breed, a group of herdswith similar characteristics such as production system, a regional or anational population.

The term “univariate data analysis” refers to data analysis in whichdata relating to a single variable are analysed. The univariate dataanalysis may comprise analysis of correlated univariate variables.

The term “multivariate data analysis” refers to data analysis in whichdata relating to at least two variables are analysed.

It should be understood that a result from the univariate ormultivariate analysis may be used as an input for further analysis. Thefurther analysis may be univariate or multivariate. For example, theoutput from a Principal Component Analysis (PCA) may be used as an inputfor a State Space Model (SSM) or vice versa.

In a further aspect, the invention provides a method for observing andpredicting a physiological state of an animal, the method comprising:

-   -   repetitively providing at least one sample of a body fluid of        the animal,    -   analysing the at least one sample, so as to obtain at least one        sample value of each of a plurality of parameters of the body        fluid,    -   entering the sample value of each of the parameters in a        database of a computer system,        whereby the database is loaded with multiple database entries        representing the sample value of each of the parameters at        various points in time, and    -   performing a univariate analysis of the database entries to        obtain a first set of data representing expected values of at        least one of the parameters at future points in time,    -   performing a multivariate analysis of the database entries to        produce a second set of data derived from combined analysis of        sample values of at least two of the parameters, and    -   combining the first and second sets of data to obtain a third        set of data representative of the physiological state of the        animal, and    -   storing each of the first, second and third sets of data in the        database.

The computer and the database need not be located in the same physicallocation. For example, the computer, including the processor, may becomprised in an analysis apparatus provided near an animal herd, forexample in a stable, whereas the database may be comprised in a personalcomputer in an office separate from the stable, or in a mainframelocated at a remote data processing facility.

In yet a further aspect, the invention provides a method for observingand predicting a physiological state of an animal, the methodcomprising:

-   -   entering at least one external value of at least one external        parameter in a database of a computer system,    -   repetitively providing at least one sample of a body fluid of        the animal,    -   analysing the at least one sample, so as to obtain at least one        sample value of each of a plurality of parameters of the body        fluid,    -   entering the sample value of each of the parameters in the        database, whereby the database is loaded with multiple database        entries representing the sample value of each of the parameters        at various points in time, and    -   performing data analysis of the database entries to obtain an        indication of the physiological state of the animal, whereby the        external value is included in the data analysis.

A system for performing the method is also provided.

The at least one external parameter may comprise at least one of: theage of the animal, the breed of the animal, the weight of the animal,the reproduction history of the animal, feeding particulars, season,geographical location, identification to the herd of origin, sampleyield, duration of sample flow, sample temperature, electricalconductivity of the sample, cell count of the sample, residues ofantibiotics in the sample, fat content of the sample, protein content ofthe sample, bacteriological examination of the sample, activity of theanimal, animal behaviour, feed intake, body temperature, bloodcomposition, vaginal mucus resistance of the animal, breath and noise.

In the present context, the term “identification to the herd of origin”relates to the recognition of a single animal or a herd of animals inorder to use the information in other systems either at the samelocation or at distant locations.

By taking into account external data, more accurate predictions may bemade, and the number of models to be employed in multivariate dataanalysis may be reduced.

In another aspect the invention relates to a method and a system forobserving and predicting a physiological state of an animal, the methodcomprising:

-   -   repetitively providing at least one sample of a body fluid of        the animal,    -   analysing the at least one sample, so as to obtain at least one        sample value of each of a plurality of parameters of the body        fluid,    -   entering the sample value of each of the parameters in a        database of a computer system,        whereby the database is loaded with multiple database entries        representing the sample value of each of the parameters at        various points in time, and    -   performing State Space Model (SSM) analysis of the database        entries to obtain data representative of the physiological state        of the animal.

The invention also provides a method and a system for observing andpredicting a physiological state of an animal, the method comprising:

-   -   repetitively providing at least one sample of a body fluid of        the animal,    -   analysing the at least one sample, so as to obtain at least one        sample value of each of a plurality of parameters of the body        fluid,    -   entering the sample value of each of the parameters in a        database of a computer system,        whereby the database is loaded with multiple database entries        representing the sample value of each of the parameters at        various points in time, and        performing a multivariate projection analysis of the database        entries to obtain data representative of the physiological state        of the animal.

Specific embodiments and features of the aspects of the invention areapparent from the appended claims and the below detailed description ofthe invention. It should be understood that the below description is inno way limited to particular aspects of the invention. Rather, thediscussion applies equally well to any aspect of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be further described with reference to thedrawings, in which:

FIGS. 1 and 2 illustrate measured cell counts vs. analysis data forvarious cows,

FIG. 3 contains a chart (chart A) of simulated data and charts (chartsB, C and D) representing output from an extended multiprocess Kalmanfilter.

FIG. 4 shows a general diagram of the information flow according to thepresent invention.

FIGS. 5 a and 5 b show information flow according to en embodiment ofthe present invention when analysing for BHB and ketosis.

FIGS. 6 a and 6 b show information flow according to an embodiment ofthe present invention when analysing for mastitis indicators (e.g.NAGase or LDH) and mastitis.

FIGS. 7 a-7 d shows information flow according to an embodiment of thepresent invention when analysing for progesterone and reproduction.

FIG. 8 shows information flow according to an embodiment of the presentinvention when analysing for urea and protein status.

FIG. 9 shows information flow according to an embodiment of the presentinvention when analysing for milk fat, milk protein and energy status.

DETAILED DESCRIPTION OF THE DRAWINGS

One tool for multivariate data analysis is Principal Component Analysis(PCA), in literature also referred to as “factor analysis”. In short,manifest variables are substituted by latent variables in multivariatedata analysis. Manifest variables are direct and measurable, i.e.manifested variables, in the present context also referred to as samplevalues, such as fat- or lactose concentration in milk. Latent variablesare weighted sums of the manifest variables. As an example, latentvariables t₁ and t₂ are determined as t₁=0.45*fat %+0.12*lactose %, andt₂=0.05*fat %+0.72*lactose %. Here, t₁ and t₂ are projections of themanifest variables, fat % and lactose %, on a vectors [0.45; 0.12] and[0.05; 0.72]. By appropriate selection of weightings, e.g aseigenvectors of a matrix of manifest variables, the thus determinedlatent variables include information from all of the manifest variablesindependently from the number of manifest variables. Accordingly,information in an aggregation of data may be distinguished or separatedfrom random noise. Moreover, the weightings may be visualised, so as toenable extraction of information related to the manifest variables, andthe latent variables may be visualised, so as to enable extraction ofinformation concerning objects, for example animals, such as cows, onwhich measurements have been performed.

The manifest variables may be provided by using any analytical meansknown in the art. Illustrative examples of such analysing means includesenzyme based assays, immunologically based assays, biosensors,biochemical assays, spectrometric assays, wet chemistry assays,sequential injection analysis and flow injection analysis assays whichare suitable for the analysis. Preferably, the analysing means aredesigned to perform quantitative measurements. In one useful embodimentthe analysing means comprises solid support analytical means or deviceswhich e.g. may be in the form of test strips (also known as dry sticks)comprising appropriate reagent(s) that in the presence of the compoundbeing analysed generate(s) a detectable signal. Additionally, theanalysing means may comprise or may be operationally linked to means forstoring and transporting such solid support analytical devices.

The aggregation of data may conveniently be arranged or stored in atable in the database. For example, measured variables may be arrangedin columns of the table, and the objects, e.g. identifications of cows,may be arranged in rows. This table is referred to as X. In PCA, theabove-mentioned weightings can be the elements in the eigenvectors tothe correlation matrix of X. The number of relevant eigenvectors, whichgoverns the number of relevant latent variables, is dependent from thecontent of information in X. As an example, table sizes of 30-1000×20could yield a number of latent variables of, e.g. 2 to 30, such as 2 to20, usually 2 to 8.

A physiological state of an animal, such as the animal's health state,may be determined from a comparison of a pattern in measured parameters,i.e. sample values, and a reference pattern (or a reference parametervalue) which is typical for healthy animals and a pattern which istypical for animals suffering from a certain disease, respectively. Oncean aggregation of data covering all physiological states to be observedor predicted is available, probabilities of a particular animalbelonging to the various states may be determined. If for example,sample values of a particular cow are determined during and aftermilking of the cow, the cow may be classified, and appropriate measuresmay be taken.

In multivariate data analysis, so-called patterns of parameters (i.e.manifest variables) may be provided in order to take into account mutualinfluences between parameters. If a selective parameter is at hand,univariate data analysis may be appropriate. As an example, oneprogesterone measurement may in most cases give a satisfactoryindication of oestrus or heat, and pregnancy. However, it has been foundthat very few parameters are purely selective for the physiologicalstate of, e.g. a cow, and a qualified assessment is in most cases onlypossible following an analysis of sample values of several parameters.For example, a high level of urea in milk may indicate one state if thefat concentration is high, and another state if the fat concentration islow, while an increasing or decreasing milk yield may indicate yet thirdand fourth states.

In order to predict the contents of somatic cells in milk, amathematical method, such as PLSR (Partial Least Squares Regression),may be employed, for analysing an Infrared (IR) spectrum of milk. It hasbeen found that such a model is suitable for establishing a basis ofexperience for each individual animal in a herd.

In order to establish a basis of experience which is global, i.e. notspecific for an individual animal but generally applicable to all animalof a certain kind, e.g. cows, chemometric classification methods may beapplied. One such method is the so-called Soft Independent Modeling ofClass Analogies (SIMCA), in which previous (or historical) sample valuesare grouped in classes, whereby the classes are analysed individually bymeans of PCA. Thus, historical sample values may for example be groupedin one class representing healthy cows, and another class representingsick cows. A model for each class may exist, and by applying new samplevalues to the models of the various classes, it may determined to whichclass the cow in question belongs. However, such a methodology does nottake into account external data, such as race, age, feeding particulars,season, geographical location, etc. which may influence the sample data.

Therefore, an “inverse SIMCA” has been developed in which one separatemodel is established for each healthy animal. If a new measurement doesnot resemble any one of the patterns of healthy animals, the probabilityof the animal in question being sick is high. Thus, by introducing aplurality of separate models, wider limits are created for what isregarded as a normal state which also reflects the biological reality.

However, the number of models may be reduced, or reliability of analysisresults increased, by including the external data in the models.

In one embodiment of the models, the animals are grouped by theircalving time. A healthy state, i.e. no mastitis, is defined by a cellcount of less than 200. The analysis only includes animals for which thenumber of observations with a low cell count is at least 30. Thisresults in a total of 121 models which are based on 19 variables orparameters: milk yield, FPD and Conductivity, as well as relative andabsolute values of Fat A, Fat B, Protein, Lactose, Urea, Citric acid,Lactate Dehydrogenase (LDH), Total solids (TS) and Solids Non Fat (SNF).The measured cell count is not included in the models. As the variancespan for the measured parameter values varies, an auto scaling of thesample values is performed before the eigenvectors are computed.

Mathematically, the models may be expressed as:X=T _(a) *P _(a) ′+Ewherein X represents the scaled data, T represents the latent variables(the projection of X on P), P is the eigenvector of the correlationmatrix X′X, and E is a residual matrix which collects random noise. Thesubscript a indicates the dimension of the model. The dimension is foundby cross validation and is also referred to as the complexity, the 20number of latent variables, number of factors or the range of X′X. Inthe present context, a is typically at least 2 and at the most 8.

For each cow and for each milking (indexed by i), the 19 variables aresampled in a pattern x_(i), and the projection of x_(i) on each of themodels is determined:t _(i) =x _(i) *P

In order to ensure an independent validation, i.e to ensure globality,models based on the animal in question itself are omitted.

Leverage, denoted h (corresponding to Hotelling's T²), and residual, r,are subsequently determined. Geometrically, the leverage represents thedistance from the 19-dimensional point of measurement to the point ofprojection in the model. The leverage is computed as the square sum ofthe elements in t_(i), and the residual is computed as the square sum ofthe elements in the vector x_(i)−t_(i)*P′. The quantities h and r arenormalised with their respective 95% significance levels from themodelling phase. The final test quantity is the length of the vector(h,r), and x_(i) is regarded as belonging to the model if this quantityis less than √2.

FIGS. 1 and 2 illustrate measured cell counts vs. analysis data forvarious cows. The “fit to normal” data are obtained as 100*[(the numberof models to which a certain milking belongs)/(the total number ofmodels)]. Thus, if a milking belongs to 14 out of 118 models, the “fitto normal” value is 100*14/118=11.9. The cell count in FIG. 1 representsmeasured cell counts. As illustrated in FIGS. 1 and 2, when the cellcount is high, the models based on low cell counts do not fit well,whereas when the cell count is low, the models fit well. It has beenfound that the inclusion of fat, protein, solids and SNF (SolidsNon-Fat) in the models appear to improve the discrimination ability ofthe models.

As it will be understood from the above disclosure, one example of thephysiological state of a cow is whether or not the cow suffers frommastitis. In an SSM for predicting and diagnosing mastitis, the input tothe models may include somatic cell count data.

SSMs have been developed for following a process developing over time.This process may either follow a pre-planned route or deviate from that.The deviations could represent either a usually high measurement error,also referred to as an outlier, or a change in the systematic part ofthe process.

As indicated above, somatic cell count is an accepted indicator ofmastitis. However, measurements of cell count are subject to noise andoutliers, which decrease their potential use in decision support.Statistical tools to separate noise from biologically relevant changescan help improving the interpretation of somatic cell count (SCC) data.The extension (Smith and West, 1983) of the multiprocess Kalmanfilter(Harrison and Stevens, 1976) to provide probabilities of different kindsof changes may be used in decision support—for example an action oftreatment should be taken if the probability of an increase in SCC isabove a critical level. Thus, there is provided a dynamic linear modeland the multiprocess class II mixture model with recursive updatingprocedure for providing probabilities of different kinds of changes.

Model

Dynamic linear model. For the time series {y_(t)}_(t=1, . . . , n)consisting of n observations (of e.g. In(somatic cell count)) a dynamiclinear model (DLM) is described by an observation equation:Y _(t) =F _(t)θ_(t) +v _(t)a system equation:θ_(t) =G _(t)θ_(t-1) +w _(t)and initial information:θ₀˜N(m₀, C₀)where F_(t) is the observation matrix, θ_(t) is a latent vector (orscalar) and v_(t), with v_(t)˜N(0,V_(t)), is the observation noise. Thelatent process {θ_(t)}_(t=1, . . . , n) is given by the system equation(and the initial information) with evolution matrix (system matrix)G_(t) and evolution error w_(t). It is assumed that w_(t)˜N(0,W_(t)),with v₁, . . . , v_(n), w₁, . . . ,w_(n) mutually independent andindependent of the initial information. The model specified by{F_(t),G_(t),V_(t),W_(t)} will be denoted M_(t).

Example: The sire model given by em, for Y_(t)=s+e_(t), for t=1, . . . ,n; with s˜N(0,σ_(s) ²) independent of e=(e₁, . . . ,e_(n))′˜N_(n)(0,I_(n)σ_(e) ²) is equivalent to the DLM given byobservation equation Y_(t)=s_(t)+e_(t), system equation s_(t)=s_(t-1)and initial information s=s₀˜N(0,σ_(s) ²). Note that F_(t)=G_(t)=1 andV_(t)=σ_(e) ², for t=1, . . . , n; m₀=0 and C₀=σ_(s) ⁰ ², and the modelis without evolution error.

Multiprocess class II mixture model: If the observations do not followthe same DLM for all values of t, it is useful to introduce mixturemodels, where, at each time t, we may choose between J different models.The Multiprocess class II mixture model is defined as follows: Let, forsome integer J>1, A={α₁, . . . , α_(J)} denote the parameter space forα, and suppose, that at each time t, there exist an α∈ A so thatM_(t)(α) holds. If the value, α_(j), of α defining the model at time t,M_(t)(α_(j)), is selected with known probability,π(j)=P(M_(t)(α_(j))|D_(t-1)), then the series {Y_(t)}_(t=1, . . . , n)is said to follow a multiprocess class II mixture model. We will useM_(t)(j) as short notation for M_(t)(α_(j)). Furthermore we let D_(t)denote the information available at time t, t>0. Here we will assumethat D_(t)=D_(t-1)∪{Y_(t)} for t>0.

Multiprocess Kalman filter (extended): In the following we outline therecursive updating procedure for providing posterior probabilitiesP(M_(t)(j)|D_(t)), of model j at time t, as well as one and two stepback smoothed probabilities, P(M_(t-1)(j)|D_(t)) andP(M_(t-2)(j)|D_(t)), for the different models at different time points.The procedure is outlined for a model with J=4, G_(t)=G and F_(t)=F forall t. The observation error as well as system error are assumed todepend on the model at time t, but are otherwise independent of time.Model j is assumed to be selected with probabilityP(M_(t)(α_(j))|D_(t-1))=π₀(j) independently of the past, D_(t-1), j=1, .. . ,4 (fixed model selection probabilities). A priori it is assumedthat θ₀˜N(m₀,C₀) and that all of the parameters are known. For t=1: Fromthe system equation and the prior distribution of θ₀ we obtainθ₁|M₁(j),D₀˜N(Gm₀,GC₀G′+W(j)) for j=1, . . . , 4. This, together withthe observation equation, gives, conditional on M₁(j), the forecastdistribution of Y₁:Y₁|M₁(j)˜N(FGm₀, F(GC₀G′+W(j))F′+V(j))

Next, the posterior probability of the different models at time 1 arecalculated fromP(M₁(j)|D₁)∝p(y₁|M₁(j))P(M₁(j)|D₀)where P(M₁(j)|D₀) by assumption is equal to π₀(j). The posteriordistribution of θ₁ is then given by a mixture ofθ₁|M₁(j),D₁˜N(m₁(j),C₁(j)), j=1, . . . , 4 with mixture probabilitiesP(M₁(j)|D₁); For time t>1: The steps in obtaining the (an approximate)posterior distribution of θ_(t), as well as one (and two) step backsmoothed probabilities of the different states/models at time t—1(t—2for t>2) become more involved. Here we refer to Smith and West (1983)for further details.

In the following non-limiting embodiments of the present invention cowsare used as non-limiting illustrating examples of the type of animaluseable in the present invention and milk used as non-limitingillustrating examples of the type of body fluid useable in the presentinvention.

These embodiments also illustrates that the selection into the groupsIndicator based risk and Additional risk factor, of the differentfactors involved may be a dynamic process and that it is obvious for theperson skilled in the art how to provide minor or larger modificationsand changes.

EMBODIMENTS OF THE PRESENT INVENTION General Application of the PresentInvention

In a preferred embodiment of the present invention the general design,as shown in FIG. 4, has, in as far as this is possible, been applied toeach particular model. The models have 2 major outputs; an overall risk(or likelihood of an event) presented to the user, and a calculation ofwhen to take the next sample that feeds back to the analysis apparatus.2 Modules generate these outputs; one using only the informationprovided by the signal coming from the analysing apparatus, the othercombining diverse additional information into an additional risk factor.This structural separation is designed to make it easier to test thedifferent components of the model and incorporate further developments.It also reflects the underlying logic that additional risk factors areonly those factors whose effects are not acting on the signal beingmeasured. In other words, if the effect of body fat mobilisation isfully reflected in BHB levels then body fat mobilisation should not beincluded as an additional risk factor.

Two additional features are incorporated to allow the model to beadjusted to local conditions, a precision multiplier and a gainmultiplier. The precision multiplier allows the accuracy of the riskassessments to be modulated according to local requirements. An exampleof this could be a dairy farmer who supplies milk for cheese making andthus has more stringent requirement to reduce mastitis in his herd. Theprecision multiplier will allow him to increase his sampling frequencyand thus the precision of the risk as measured by the analysing signal.

The gain multiplier will allow the sensitivity of the model to beadjusted. An example of this could be a farm or region where the feedingwas such that BHB levels were systematically higher than the universalnorm and consequently an unacceptably large number of animals were beingidentified as at risk of ketosis. The gain multiplier could be then usedto decrease the sensitivity of the risk assessment to fit with priorexperience of ketosis incidence in this herd. Whether or not thisfacility will be widely used, or whether it should be accessible by theuser, remains to be determined but it is included in the modelarchitecture.

Application of the Present Invention for the Determination of BHB andKetosis

In another preferred embodiment of the present invention subclinical andclinical ketosis and BHB in e.g. milk is detected in a manner which, asshown in FIGS. 5 a and 5 b. In this system the baseline levels of BHBare expected to be substantially lower than the values found withketosis. Further, after a ketosis incident values will return to theoriginal baseline. The rate of onset of ketosis is such that weanticipate using a 3 day smoothed value to calculate risk. It isimportant to note that the bandwidth chosen for smoothing is time andnot number of samples i.e., if only one sample was taken in the 3preceding days then the smoothed value contains only that onemeasurement. This applies to all bandwidths in these models.

The basis for the ketosis model is the ketone body: beta-hydroxybutyrate(BHB), which has a strong relationship to clinical ketosis. The BHBmeasurements are used to generate an Indicator Based Risk (IBR) and anAdditional Risk based on other Factors (ARF) is also generated. Togetherthese are used to generate an overall risk of ketosis. FIGS. 5 a and 5 bdescribe the ketosis model: one describing the indicator based risk(IBR, see FIG. 5 a) and another describing the additional risk factors(ARF, see FIG. 5 b).

Note: All the ketosis model parameter names end with a capital K (forKetosis), drop the ending ‘K’ and they should be (to some degree)readable.

Unifying Components (Days to Next Sample, Output Risk and OutputReliability)

Overall Ketosis Risk (OverRiskK)

The Overall Ketosis Risk (OverRiskK) is derived from the IBR and theARFARFWK (Additional Risk Factor Weight Ketosis) is the scaling factorto weight the ARF relative to the IBR. An ARF is a factor that is not atall reflected in BHB, but still imposes a risk for the cow to getketosis. It is therefore necessary that ARF's can cause an alarm if noBHB-measurements are available, e.g. the first days after calving whereit is likely that no samples will be available.

It should also be stated that ARF is weighted against IBR, and that thisweight-factor presumably will be adjusted according to the reliabilityof the factors that contributes to the calculations of ARF and IBR.

Days to Next Sample (DNSK)

As the biological model perceives an increased risk of ketosis so thedays to next sample is reduced from a default value (DNSdefK). If a highprobability comes out from IBR, ARF or there is a high probability thata measurement was an outlier (POutlierK), we are interested in getting anew sample as quick as possible.

The model calculating DNSK is then adjusted by the Precision Multiplier.The purpose of the Precision Multiplier is to allow the user to modifythe general rate at which the milking device takes another sample i.e.the overall intensity of monitoring for ketosis. The PrecisionMultiplier works at the level of the whole model i.e., the user canadjust the sampling frequency for ketosis in general but he cannotadjust the sampling frequency for individual cows. As the name suggests,the Precision Multiplier (PrecMultK) is a simple multiplier on Days toNext Sample (DNSK).

Output Ketosis Risk (OutRiskK)

The final output risk presented to the user (OutRiskK) is generated fromthe overall risk (OverRiskK) multiplied by the Gain multiplier. The GainMultiplier (GainMultK) provides the possibility of expanding orcontracting the range of risk values being produced. It is envisaged asa means to adjust the model to local conditions, e.g. if in a particularregion/farm it is found that the ketosis model only ever produces riskvalues between 0 and 0.5 even though clinical cases of ketosis are beingobserved, then the GainMultK could be changed. Furthermore, it followsthat if GainMultK is 0, then OutRiskK=0 and therefore no cows will bepointed out with risk of ketosis in this herd.

Output Reliability (OutRelK)

The Output Reliability reflects the noisiness of the BHB signal, thesampling frequency for BHB, and the quality of the ARF information. Ifthe user has set the milking device to take very few BHB measurementsand is not entering any of the supplementary information e.g. healthrecords, then it is desirable to let the farmer know that the risksbeing generated by the ketosis model are of lower reliability. Thenoisiness of the signal can be obtained from the posterior variance inthe State Space model (SSM). This value is then multiplied by afunction, which decreases from 1 to 0 with increasing interval lengthbetween the current and previous samples (IntK) to give RelIBRK. For theARF, those factors derived from the State Space Model (SSM) (i.e.acceleration in milk yield and energy status) use the same functionalform as for the SSM, while those factors which are herd management/userinputs have one value if there is information and are zero if thatfactor is not being supplied (the following list may make more senseafter reading the section on ARF).

If all the information which goes into the ARF is perfect thenRelARFK=1. The Output Reliability is then the sum of RelIBRK and RelARFKweighted in proportion to their 2 contributions to the output risk:

Elements of the Indicator Based Risk (IBR)

The IBR is based on measurements of BHB in milk. From these, a risk dueto the level of BHB and a risk due to the rate of change of BHB arecalculated and combined to give IBR. The rate of change of BHB isconsidered an important element of the model when identifying ketosis atan early stage. Some cows have a high tolerance of ketone bodies, i.e.they have a high level of ketone bodies in the body fluids withoutshowing clinical signs. However, these cows have been considered as riskcows even though they may not need treatment, because a relatively smallincrease in BHB will take them to a high BHB level.

Baseline of BHB (BaselineK)

The BaselineK is calculated for each cow and is a smoothed level of BHB(LevelK), where each BHB-value is weighted by PNormal (a probabilityfrom SSM) to account for outliers. There is a restriction in thefunction namely that BaselineK is less than e.g. 0.15 mM, because it isunrealistic with a natural higher baseline As soon as BHB is measuredafter calving the baseline will adjust itself according to theindividual cow. It is expected that 0.15 mM is a relatively high maximumbaseline, which is useful in most herds and countries, but the maximumbaseline may be adjusted in accordance with national law in aparticularly country.

Risk Due to Slope of BHB (RiskSlopeK)

It is assumed that the greater the positive rate of change of BHB(SlopeK), the greater the risk is of ketosis. SlopeK is a smoothedoutput from SSM adjusted for PslopeK which is a probability calculatedin SSM. MaxSlopeK is a constant which will give a RiskSlopeK=1. Thesuggested value of MaxSlopeK is 0.5 due to the assumption that BHB insevere clinical cases of ketosis/left displaced abomasum can increasefrom app. 0 mM to 2 mM during a 4 days period. I.e. a slope of 0.5 whichwhen divided by MaxSlopeK (0.5) will give a RiskSlopeK=1 (if PslopeK isassumed 0). RiskSlopeK can assume values <0 and >1.

Risk Due to Level of BHB (RiskLevelK)

The higher the level of BHB (LevelK) compared to the baseline level ofthe individual cow (BaselineK), the greater the risk of ketosis. The useof a baseline with a maximum value of 0.15 relies on the assumption thatconcentrations >0.15 mM are associated with physiological imbalance thatcan mediate a subclinical or clinical ketosis. LevelK is a smoothedoutput from SSM and PLevelK is a probability calculated in SSM.MaxLevelK is a constant which will give a RiskLevelK=1. Under theassumption that a cow with 1.0 mM BHB has clinical ketosis, i.e.RiskLevelK=1, the suggested value for MaxLevelK is 1.0. RiskLevelK mayassume values <0 and >1.

Indicator Based Risk (IBR)

IBR is calculated as a weighted combination of RiskSlopeK andRiskLevelK.

The Additional Risk Factor (ARF)

As mentioned earlier, the crucial point about ARF is that the ketosisrisks included here are not already included in the IBR, i.e. anyfactor, which will affect BHB, should not be included here. In thosecases where a factor has both an effect on BHB and an additional effectit is necessary to distinguish between these two effects and onlyinclude the additional effect in the ARF.

The elements that make up the ARF are described below; they combine togive ARF as follows:

Acceleration in Milk Yield

The higher maximum milk yield (kg/day), the higher risk of ketosis, insome cases 2.5% higher risk pr. kg increase in milk yield (test day milkyield used). However, it seems more likely that it is the accelerationin milk yield that is interesting in relation to the development ofketosis. It seems logical that too heavy acc. in milk yield (MYAcc)would increase the risk of a breakdown/imbalance in the fat- andcarbohydrate metabolism of the cow and thereby increase the risk ofketosis. In this case MYAcc is an ARF because a high MYAcc presumablyprecedes a rise in BHB.

MYAcc is the slope of the milk lactation curve of the individual cow andis an output from SSM on the basis of days from calving and daily milkyield recordings. MaxAccK is a scaling constant, which defines the levelof MYAcc that will return a RiskAccK of 1. RiskAccK can assume valuesabove 1, in this particular embodiment all negative values of RiskAccKshould be converted to 0. The suggestion for this constant in this caseis 3, because this is believed to be the maximum reasonable increase inkg milk/day when it is smoothed over 3 days.

Current Lactation Disease History (CLDHRiskK)

The physiological background for the fact that other diseases oftenincreases the risk of ketosis is, that other diseases can induce adecreased feed intake that can lead to an increased mobilization.Depending on the duration and the volume of this mobilization and thecapacity of the liver to fully oxidize this fat from the adipose tissuesthe cow can develop subclinical or clinical ketosis. Choosing thediseases that could cause ketosis (and therefore should be included inthe model) is among other things based on epidemiologicalinvestigations, which are briefly presented in the following section:

The risk of ketosis was increased by e.g. milk fever. Studies have shownthat metritis significantly increases the risk of getting ketosis, whileketosis significantly increases the risk of LDA. The development of LDAis shown clearly to associate with high ketone levels and could beassumed bidirectional. But no studies have shown the effect (Odd ratio:OR) of LDA on ketosis has been estimated. Furthermore, studies haveshown a significant effect of mastitis on ketosis where there were founddifferent results depending on the definition of mastitis: OR=1,4(1,2-1,7) for acute mastitis and OR=2,4 (1,7-3,3) for chronic mastitis.This could imply that different types of mastitis (bacteriatypes) havedifferent effects on the risk of getting ketosis. Furthermore, a reasonfor discrepancies between studies could be a difference in thedistribution of mastitis in relation to calving date. Mastitis does notseem to affect the risk of getting ketosis.

A function is used to calculate the risk of ketosis due to a givendisease (DisRiskK) as a function of days since occurrence (DisDaysK), anexpected risk period, i.e. the day where the disease no longer isbelieved to be a risk for ketoisis (DisTK) and the severity of thedisease (DisSevK).

In table 2 different diseases are listed according to their suggestedmaximum risk (MaxDisK). DisRatK determines the shape of the curvebetween MaxDisK and DisTK, i.e. how DisRiskK changes from the day oftreatment until the risk period has expired. DisTK gives the number ofdays until the risk has reduced to 0.36 (exp(−1) or 1/e).

TABLE 1 List of diseases according to their maximum risk. Disease(DisTypek) MaxDisK DisTK (days) DisRatK LDA (left displaced abomasum)0.9 10 0.4 RDA (right displaced 1.1 10 0.4 abomasums) Rumen Acidosis 0.710 0.4 Milk fever 0.6 8 0.4 Retained placenta 0.3 12 0.15 Metritis 0.512 0.15 Mastitis 0.2 8 0.4 Other 0 0 0

Diseases can not be considered totally independent and therefore itcould lead to overestimation of the ARF, if the risk of each disease isadded together in a cow that experiences several diseases at the sametime (e.g. mastitis and metritis). Therefore, it is always the diseasewith the highest risk factor at the given day that counts in thecalculation of the total ARF. If a cow has experienced repeatedlytreatment of the same disease within a lactation, then only the lastincidence of this particular disease should count in the calculation ofDisRiskK. Thus, it is necessary that DisRiskK is calculated every dayfor each of the last incidence of the diseases in the current lactation.

Lifetime Number of Ketosis

The background for including a cow's history of ketosis is the followingfacts: there is approximately 2.5 times higher risk of getting ketosisif the cow had ketosis in the previous lactation. Estimates shows thatcows treated for ketosis in the first lactation have a 17% risk ofketosis in the second lactation, while those that were not treated inthe first lactation have a 4% chance of ketosis in the second lactation.The equivalent values have been shown to be 8 and 3% and a cow with milkfever in a previous lactation has 2-5 times higher odds of milk feverand a cow with ketosis in a previous lactation has 4-12 times higherrisk of getting ketosis again.

The ARF due to earlier incidences of ketosis is assumed constant duringlactation because it expresses a constant susceptibility or differentsensitivity between cows. Therefore, it is considered a genetic factor.The additional risk from earlier incidences of ketosis may be includedvia a constant for the cows history (HistConK). The constant isdepending on the history of ketosis

Energy Status (EnStat)

In the situation where the nutritional environment is limiting,excessive mobilisation of body energy reserves may occur puttingpressure on the cow's physiological balance. Energy Status is a measureof this, it is also the output of the Energy Status Model. It isexpected that poor energy status will increase the risk of ketosis. ThisEnergy Status may be included as an ARF.

List of Inputs and Outputs

Suggested Inputs Parameter Comment CowID Unique ID, duplicates notallowed RunTimeK Date and time of the current run BHBTimeK Date and timeof the latest BHB sample GainMultK Gain Multiplier - input for adjustingto local conditions PrecMultK Precision Multiplier - input for adjustingto local conditions LevelK Concentration of BHB - smoothed output of SSMSlopeK Rate of change in BHB - smoothed output of SSM IntK Time intervalbetween the latest and the previous BHB samples POutlierK* Probabilityvalue from SSM PNormalK* Probability value from SSM PSlopeK* Probabilityvalue from SSM PLevelK* Probability value from SSM DisDateK Date andtime of disease DisTypeK Code for different diseases # DisSevK Codes:possible = 0.3, probable = 0.6, definite = 0.9 DFC Days From Calving -should be validated against prior pregnancy and AI records. MYAcc Slopeof the milk lactation curve - smoothed output of SSM based on daily milkyield recordings and DFC from Herd Man. Sys. EnStat Energy status,output of another model *Must be available both as estimates to time tand one step back-smoothed the prior estimate (t − 1). # Left displacedabomasum, right displaced abomasums, rumen acidosis, milk fever,retained placenta, metritis, mastitis, ketosis and other.

Synchronicity of Inputs

The model is built on the basis that a new BHB-value triggers the modelto run. Furthermore, a new sample/calculation may also be triggered whena disease is entered, i.e. when DisDateK is input.

Suggested outouts to the end-user Parameter End-User Comment DNSKMilking device Days to next sampling OutRiskK Cow Manager Output Risk ofKetosis OutRelk Cow Manager Reliability of Output Risk of Ketosis

Application of the Present Invention for the Determination of Mastitis

Matitis Indicators may be e.g. LDH or NAGase. In yet a preferredembodiment of the present invention Mastitis Indicators and mastitis isdetected in animals, as shown in FIGS. 6 a and 6 b. There are manysimilarities between the mastitis model and the ketosis model. There aretwo major differences from the ketosis model. The first is that thebaseline Mastitis Indicator level does not necessarily return topre-infection levels. The second is that there are some population orgroup level milk quality requirements.

Note: All the mastitis model parameter names end with a capital M (formastitis), drop the ‘M’ and they should be (to some degree) readable.

M del Basis

The basis for the mastitis model may be lactate dehydrogenase (LDH)which in this embodiment of the present invention is selected forillustrative purposes only. It is assumed that there is a strong linearrelationship between LDH and udder health status.

The LDH measurements are used to generate an Indicator Based Risk (IBR),an Additional Risk based on other Factors (ARF) is also generated.Together these are used to generate an overall risk of mastitis. Thestructure of the model is shown in FIGS. 6 a and 6 b.

An important issue concerns the effects of dilution. Assuming that LDHis produced in response to an amount of milk cell damage then the amountof LDH may be a more relevant indicator of mastitis than theconcentration of LDH in the milk. Clearly, if this is the case then theconcentration of LDH which constitutes a mastitis alarm will bedependent on milk yield. In addition, mastitis usually occurs in onlyone quarter at a time and the milk from that quarter is diluted by milkin the other three quarters. A further complication is that the ratio ofmilk amounts produced by the infected and healthy quarters is alsoaffected by mastitis. Consequently, it may be decided to use LDH amountand not LDH concentration as the indicator variable in the biologicalmodel. This amount (LDH) is the input to the biological model, it isgenerated from the LDH concentration (LDHconc) and milk yield at thatmilking (MY).

Dealing with Acute Cases Versus Chronic Cases of Mastitis

Acute cases of mastitis are characterised by a sudden rise in the levelof the mastitis indicator i.e., LDH, whereas chronic cases are typifiedby a high and relatively stable level of LDH. The architecture of themastitis model for identifying acute cases is very similar to thegeneral model structure FIG. 4 and to the model for ketosis inparticular FIGS. 5 a and 5 b. These acute cases should generate a highRisk of Mastitis.

Chronic cases will not necessarily feature in the Risk of Mastitis butwill instead generate a high value in the Milk Quality Risk. This “milkquality” module of the mastitis model is based on the stable level ofLDH relative to the herd average. The key to distinguishing betweenacute and chronic mastitis is in the calculation and use of the stablelevel.

Unifying Components (Days to Next Sample, Output Mastitis Risk andOutput Reliability)

Days to Next Sample (DNSM)

As the biological model perceives an increased risk of (acute) mastitisso the days to next sample is reduced from a default value (DNSdefM). Inaddition to the effects of a high indicator based risk (IBR) oradditional risk factor (ARF), if the latest LDH value has a highprobability that it is a positive deviation from the normal time seriesthen another sample is taken quickly. Also, if there is a highconductivity measurement (CondM) a follow-up sample is quickly taken.

The model calculated DNSM is then adjusted by the Precision Multiplier.The purpose of the Precision Multiplier is to allow the user to modifythe general rate at which the milking device takes another sample i.e.the overall intensity of monitoring for mastitis. As the name suggests,the Precision Multiplier (PrecMultM) is a simple multiplier on Days toNext Sample (DNSM)

Output Mastitis Risk (OutRiskM)

The Mastitis Risk presented to the user (OutRiskM) is generated from theOverall Mastitis Risk (OverRiskM), which is derived from the IBR and theARF.

IBR is the indicator based risk and ARF is the risk due to theadditional risk factors. The final output risk (OutRiskM) is calculatedfrom the overall risk by applying a multiplier, the Gain Multiplier. Thegain multiplier provides the possibility of expanding or contracting therange of risk values being produced. It is envisaged as a means toadjust the model to local conditions, e.g. if in a particularregion/farm it is found that the mastitis model only ever produces riskvalues between 0 and 0.5 even though clinical mastitis cases are beingobserved, then the Gain Multiplier could be changed. The wisdom of doingthis and who should be given the access privilege remains to clarified.

Output Reliability

The Output Reliability reflects the noisiness of the LDH signal, thesampling frequency for LDH, and the quality of the ARF information. Ifthe user has set the milking device to take very few LDH measurementsand is not entering any of the supplementary information e.g. healthrecords, then it is desirable to let the farmer know that the risksbeing generated by the mastitis model are of very low reliability. Thenoisiness of the signal can be obtained from the posterior variance inthe SSM. This value is then multiplied by a function which decreasesfrom 1 to 0 with increasing interval length between the current andprevious samples (IntM) to give RelIBRM. For the ARF, those factorsderived from SSM use the same functional form as for RelIBRM, whilethose factors which are herd management/user inputs have one value ifthere is information and are zero if that factor is not being supplied(table 4 indicates the reliability of different types of measurements:

TABLE 2 Indicates the reliability of different types of measurements.Name Abbreviation Reliability Acceleration in milk yield RelMYAccM post.var. function with maximum = 0.4 Milking Duration RelMilkDurM post. var.function with maximum = 0.1 Peak Milk Flow RelPMFM post. var. functionwith maximum = 0.05 Udder Characteristics RelUddM maximum = 0.2, 0 ifmissing Herd Mastitis Level Not included in reliability CurrentLactation Disease RelCLDHM maximum = 0.15, 0 if missing Energy StatusRelEnStatM post. var. function with maximum = 0.1 Conductivity Notincluded in reliability

If all the information which goes into the ARF is perfect then RelARFM32 1. The Output Reliability is then the sum of RelIBRM and RelARFMweighted in proportion to their 2 contributions to the output risk.

Elements of the Indicator Based Risk (IBR)

The Indicator Based Risk is simply the sum of two risks; the risk due tothe level of LDH and the risk due to the rate of change in LDH level.These and the associated elements are detailed below.

Stable Level (StableM)

This is basically, for each cow, an average value of the LDH level(LevelM) calculated over a time interval such as 7 days (StableIntM). AsLevelM already accounts for the probabilities of being a normal valueetc, there is no further need to account for this in the calculation ofthe stable level (StableM).

A default value of StableM might be needed at calving for thecalculation of the risk due to LevelM, this should be set low and bediluted out as actual LevelM data accumulates.

Risk Due to Slope (RiskSlopeM)

It is assumed that the greater the rate of change of LDH (SlopeM), thegreater the risk of mastitis. RiskSlopeM is increased if there is a highprobability that it is a deviation from the normal time series, i.e. aslope change (PSlopeM).

Risk Due to LDH Level (LevelM)

It is assumed that the higher the level relative to the stable level(StableM), the greater the risk of mastitis. The use of the stable levelas a baseline facilitates the differentiation between acute and chronicmastitis but it also relies on the assumption that the increase in LDHdue to an acute case is independent of the underlying stable level. Ifthis is not the case, for instance if the level of LDH associated with a“full-blown” mastitis is absolute, then the model will need to use adifferent baseline, such as that used in the ketosis model.

The Additional Risk Factor (ARF)

The crucial point about the Additional Risk Factor is that the mastitisrisks included here are not already included in the Indicator Based Riski.e. any factor which will affect LDH should not be included here. Inthose cases where a factor has both an effect on LDH and an additionaleffect it is necessary to distinguish between these two effects and onlyinclude the additional effect in the ARF. The elements that make up theARF are described below, they combine to give ARF

Acceleration in Milk Yield (MYAcc)

This is used as an index of the degree of physiological stress that thecow is experiencing. MYAcc is a way of combining milk yield and daysfrom calving which we believe crystallises the components of these twofactors which are relevant to the physiological stress being experiencedby the cow. MYAcc is highest immediately after calving and is higher forhigher yielding cows.

Given that there is a biometric model for milk yield then, in principle,the slope of the smoothed milk yield curve i.e., acceleration in milkyield, is readily available. Additionally, MaxAccM is a scaling constantis needed to give the level of acceleration which will return a risk of1.

Duration of Milking

This is assumed to index the negative physical effect of machine milkingon the cow's teat defenses to invasion by mastitis causing pathogens.The longer the duration of milking (MilkDurM), beyond some lowerthreshold, the greater the effect.

Udder Characteristics

The quantification of the cow's own susceptibility to mastitis is in 2parts; that information which is categorical (generally input by theuser), and that which is on a continuous scale. These are essentiallyrisk factors which are not expected to change on a short timescale. Thecategorical udder characteristics are currently:

TABLE 3 Udder characteristics Udder Characteristic Name Increase in riskShort teats ShortM 0.1 Low udder LowM 0.05 Leaky teats LeakyM 0.15

Peak Milk Flow Rate and Lifetime Number of Mastites

The continuous factors may be peak milk flow rate and lifetime number ofmastitis. Peak milk flow rate (MilkFlowM) should be available from themilking system at each milking where a sample is taken. In the presentcontext, peak milk flow rate is an index for teat canal diameter, thisdoes not change markedly through lactation. However, peak milk flow rateitself is affected by milk yield because the pressure created in theudder by the alveolar contraction during the milk ejection reflex willdepend on how full the udder is. Thus, this could be measured at aparticular stage of lactation or yield. One way of approximating this isto use the maximum recorded peak milk flow rate. As this routine isvulnerable to noisy measurements, the input peak milk flow rate shouldbe the output of an SSM. The peak milk flow rate (PeakMFM) is convertedto a risk factor.

The lifetime number of mastites could be used as a measure of the cow'ssusceptibility to mastitis in the same way that the lifetime number ofketoses is used in the ketosis model. However, there is a crucialdifference in that mastitis is an infectious disease and ketosis is not.Thus, to some extent we can expect the lifetime number of mastites toreflect the disease pressure placed on the cow by the environments shehas been in. For this reason this is not considered being a variable ofchoice as a predictor of the cow's susceptibility but it has thepractical advantage of being automatically generated if the risk ofmastitis output is used to generate disease incidences.

Herd Mastitis Level

Given that mastitis is an infection, the more mastitic cows in the herd,the higher the infection pressure on any given cow in the herd. The HerdMastitis Level can be calculated by combining the individual infectionburdens of the cows in the herd which are assumed to be reflected in thestable LDH amounts (StableM).

Current Lactation Disease History (CLDHRiskM)

This is analogous to the same factor in the ketosis model except thatthe distinction is made between diseases that indicate increasedinfection burden (metritis, teat tramp, acidosis) and those which justadd to the general stress the cow is experiencing ketosis, milk fever,retained placenta and other. For each of these, there is an increasedrisk of mastitis on the day of occurrence that then decays to zero overtime.

At any one time, there may be more than 1 disease risk in operation,these are combined in the following way. Within each class of disease(infection vs general) the greatest DisRiskM is chosen i.e. it isassumed that within class risks are not additive. The overall currentlactation disease history risk (CLDHRiskM) is the sum of two DisRiskMs,the highest within each class.

Energy Status (EnStat)

In the situation where the nutritional environment is limiting,excessive mobilisation of body energy reserves may occur puttingpressure on the cow's physiological balance. Energy Status is a measureof this, it is also the output of the Energy Status Model. It isexpected that poor energy status will increase the risk of mastitis(among others). Thus, Energy Status is included as an ARF.

Quarter Level Conductivity

Conductivity has 3 advantages as a measure; it is very cheap, veryquick, and at quarter level. These advantages should be taken advantageof when the milking system has conductivity. However, it is assumed thatLDH is the more reliable indicator. Therefore the risk due toconductivity (RiskCondM) is given a relatively low weight in the ARF

The 2 most important roles of the conductivity input are to trigger anew sample and to identify which quarter is mostly likely infected.

Milk Quality Risk—Dealing with Chronic Mastitis Cases

In it's simple form this could just be an output list of individualStableM values. By using a correlation between LDH and SCC it ispossible to predict bulk milk SCC and thus judge whether any cows shouldhave their milk withheld from the bulk tank.

List of Inputs and Outputs

Suggested Inputs

Parameter Comment CowID Unique ID, duplicates not allowed RunTimeM Dateand time of the current run LDHTimeM Date and time of the latest LDHsample LevelM* LDH amount - smoothed output of SSM SlopeM* rate ofchange in LDH amount - smoothed output of SSM IntM Time interval betweenthe latest and the previous LDH samples POutlierM* probability valuesfrom SSM PNormalM* probability values from SSM PSlopeM* probabilityvalues from SSM PLevelM* probability values from SSM CondtimeM Date andtime of a conductivity measurement CondM Conductivity alarm value - avalue between 0 and 1 QuarterM The quarter which the conductivity alarmis detecting PMM Precision Multiplier for mastitis DFC Days fromCalving - should be validated against prior pregnancy and AI records.MYAcc Slope from the SSM on milk yield (kg/d/d) MilkDurM Duration ofmilking (s) UdderM Udder characteristics eg. leaky teats see eqn 9 forthe full list PeakMFM Level_(t-1) from an SSM on peak milk flow rateDisTypeM Code for type of disease type# DisDateM Date of firstidentification of disease DisSevM Code for severity of disease: mild =0.3, average = 0.6, severe = 0.9 EnStat Energy status, output of anothermodel GainMultM Gain multiplier. privileged access input for adjustingto local conditions *Must be available both as estimates to time t andone step back-smoothed, the prior estimate (t − 1). #Metritis, teatinjury, acidosis, ketosis, milk fever, retained placenta, and other.

Suggested Outputs to the End-User:

Parameter Comment DNSM Days to next sampling OutRiskM Output risk foracute mastitis QuarterM Quarter at risk ReliabilityM Reliability ofoutput risk StableM stable level of LDH - indicator of chronic mastitisBulkSCCM herd mean stable level - adjusted

Application of the Present Invention for the Determination ofProgesterone and Reproduction

In another preferred embodiment of the present invention reproductionand progesterone is detected in animals, as shown in FIGS. 7 a-7 d. Thismodel is in structure more complicated than the models for ketosis andmastitis because it may be of interest that a larger number ofconditions may be identified. After calving, the cow progresses through3 reproductive states in sequence; postpartum anoestrus, oestruscycling, pregnant. These are referred to as status 0, 1, and 2respectively. During the postpartum anoestrus progesterone levels arelow, the end of this phase is characterised by the first, usuallysilent, oestrus. Detection of the first oestrus changes status to 1.Status is changed to 2 after artificial insemination. If the cow isfound to not be pregnant then status reverts to 1.

Within each state the progesterone profile may be used to indicate bothnormal and abnormal progressions. These are (status shown inparenthesis); prolonged anoestrus (0), luteal ‘cysts’ (1), follicular‘cysts’ (1), pregnancy loss (2). FIG. 7 a gives the overall modelarchitecture, the decision cascade needed to identify the differentconditions is shown in the stipled box. An expanded structure of thiscascade is described in FIGS. 7 b-7 d for each of the three states,respectively.

The framework for the reproduction model is based on the cow alwaysbeing in one of three reproductive states (StatusR), these are:

-   StatusR=0 Postpartum anoestrus, (FIG. 7 b)-   StatusR=1 Oestrus cycling with a very low likelihood of pregnancy,    (FIG. 7 c)-   StatusR=2 Potentially pregnant, (FIG. 7 d)

In the model these states are mutually exclusive. The definition of eachreproductive Status, the inputs, outputs and assumptions associated witheach Status are described in detail below. A general assumption thatapplies to the whole reproduction model is that progesterone is thedefinitive measure of reproductive status. This means that althoughother information, such as external oestrus detections (EODR), is usedit is never allowed to override the information provided byprogesterone. In the terms of the general model design, progesteroneequates with the “indicator based risk”, EODR and other factors such asenergy status equate with the “additional risk factors” (see the modeloverview in FIG. 7 a). However, because the model has the 3 StatusRsubunits it is not so easy to hold on to the IBR-ARF structure. Althoughit is not obvious within the biological model structure, the SSM forprogesterone is important. This is evident when one considers thatexpected progesterone values in the follicular phase may range from 0 to3.5 ng/ml and in the luteal phase to range from 4.5 to 50 ng/ml (mean15-20).

Note: All the reproduction model parameter names end with a capital R(for reproduction)—drop the ‘R’ and they should be (to some degree)readable.

Triggering a Run of the Model and Handling Time

The model is triggered either by a new progesterone value or by anexternal oestrus detection. In either case, the preceding value of theother is made available. This complicates the use of time in the model.

External Oestrus Detection

Indications of oestrus behaviour are derived from external oestrusdetection methods (EODR). EODR refers to all other i.e.,non-progesterone, oestrus detection methods or devices. In order for themodel to interface with the multitude of devices available the EODinputs are defined as follows:

-   EODTimeR=The date and time of the external oestrus detection. This    may differ from the time at which this information becomes available    to the biological model, both because the run may have been    triggered by a new progesterone value and because EOD values may not    be real-time inputs (eg. visual oestrus detection records).-   EODinR=the strength, or likelihood, of the oestrus detected—on a    scale from 0 to 1—supplied by the herd management system. For visual    oestrus detection it is envisaged that the farmer would be able to    choose between “possible”, “probable” and “definite” oestrus. These    would correspond to EODinR values of eg. 0.5, 0.7 and 0.9.-   EODTypeR=defines the type of device used for the detection. This is    used to assign a decay rate to the EODinR value i.e. how much weight    should I give to an EOD signal that is, for instance, a day old? For    each EODType, there are 3 constants; EODRatR, EODTR, and EODgainR.    This latter constant allows us to adjust original oestrus strength    (EODinR) internally should this be necessary.

EODR may then be calculated from these inputs to decline with timebetween current run time and EODtimeR. There might also be included aprocess for accumulating repeat EODs. The logic for this is thatrepeated evidence of behavioural oestrus should strengthen the“diagnosis” of oestrus.

StatusR=0 Postpartum Anoestrus (FIG. 7 b)

This is the period from calving up to the first oestrus which thencauses a subsequent rise in progesterone above the lower progesteronethreshold (LThreshR). A key assumption for the functioning of the modelis that the precision of the progesterone signal entering the biologicalmodel is such that real differences in progesterone between the baselineanoestrus and the threshold (LThreshR) levels are distinguishable frommeasurement error.

At calving, or when first registered by the model (for cows that don'tstart at calving), a cow is assigned Status 0. In order to progress toStatus 1 evidence of a first oestrus is required. This can be either anincrease in progesterone or a very strong indication of oestrusbehaviour. This latter means an EODR value greater than the EODthreshold (EThreshR). It is not expected that the transition from Status0 to Status 1 will be triggered by an EOD. However, it is included onthe basis that the types of first oestrus likely to be strong enough tocause a high enough EOD would be relatively late first oestruses i.e.potentially valuable and thus (possibly) worth inseminating to.

The most likely trigger for the transition to status 1 should be thefirst rise in progesterone to a level higher than LThreshR. As this isan important anchor point for subsequent sampling frequency andreproductive statistics the farmer must be sure that the sample or themeasurement is not just an outlier. Therefore, two consecutiveprogesterone values above LThreshR will likely be required to cause theshift to Status 1.

If the cow remains in Status 0 (i.e. no evidence of onset of oestruscycling) then the risk of prolonged anoestrus (ProAnOestR) and days tonext sample (DNSR) may be calculated from the EODR value and theLikelihood of Onset of Oestrus Cycling (LOOCR). LOOCR is an estimate ofthe biologically expected length of postpartum anoestrus. The basic ideaof LOOC is that the longer from time since calving the greater thelikelihood of the onset of oestrus cycling. The likelihood increasesfrom 0 to a maximum (PropCanR) that reflects the fact that there willalways be a proportion of cows which, due to reproductive malfunction,never start cycling.

The whole curve may be shifted “horizontally” by a breed factor(BreedLOOCTR) and by energy status (EnStat). In other words, the averagecurve for the breed-parity is now adjusted for that particular cows'energy status (a calculation from another model). In a similar way, the“slope” of the LOOCR function may be modified by individual reproductivehealth problems according to their type (ProbTypeR) and severity(ProbSevR). The risk of prolonged postpartum anoestrus (ProAnRiskR) issimply LOOCR adjusted down for any evidence of EOD activity. Cows with ahigh risk of prolonged postpartum anoestrus are those for which no goodbiological explanation can be found for the anoestrus—because LOOCR isadjusted for individual cow's biological modifiers of the anoestrusperiod. Days to next sample (DNSR) is calculated in a similar way sothat a default DNS (DNS0defR) is decreased by; increasing EOD and alsoincreasing risk of prolonged postpartum anoestrus. In other words, ifthere is any hint of oestrus activity or if there is a possible problemthen we want to follow that cow more closely. DNSR is calculated at anumber of places throughout the reproduction program reflecting thenumber of different exit points of the model back to the next samplingbut in any one run only one calculation is made. In FIGS. 7 b, 7 c and 7d, these exit points are indicated by a looping arrow. This value isalways adjusted by the Precision Multiplier (PrecMultRfunc) which is theinput the user has to increase or decrease the average frequency ofsampling.

StatusR=1 Oestrus Cycling with a Very Low Likelihood of Pregnancy (FIG.7 c)

This part of the reproductive model deals with three conditions:oestrus, prolonged follicular phase and prolonged luteal phase.

The Follicular Phase (Low Progesterone)

Once the cow is cycling, oestrus is defined by the first occurrence of aprogesterone level below LThreshR. It is assumed that oestrus cannotoccur if progesterone is above LThreshR irrespective of any otheroestrus signs. Identification of oestrus causes a change in reproductivestatus to StatusR=2, potentially pregnant irrespective of anyinsemination.

In order to prevent oestrus being indicated for each successiveprogesterone below LThreshR a counter (CountR) is used and oestrus isonly indicated for the first progesterone value below LThreshR. Foroestrus to be indicated, there may be a requirement that the slope ofthe progesterone profile (SlopeR) is less than a threshold (SThreshR).Once oestrus has been indicated, the model estimates how long to waitbefore inseminating (TimeToAIR) and the likelihood of the proposedinsemination being successful (LikeAISuccR). The default time lag to AIis the assumed oestrus to ovulation interval, OestOvulntR, this is thenreduced if there is any indication of behavioural oestrus (EODR). Thelikelihood of the proposed insemination being successful (LikeAISuccR)is based on an ideal conception rate (MaxConR) which can then be reducedby 4 different factors: energy status, oestrus number, length of theprevious oestrus cycle, and the slope of the progesterone profile.

CyclenR is a smoothed value for the cycle length which would be expectedfrom previous cycle lengths for that cow. CyclenR is updated at eachoestrus. Additionally, at oestrus; the oestrus number (OestNR) isincreased by 1, CountR is increased by 1 (thus preventing a repeatoestrus indication within that follicular phase), the date of theprevious oestrus (DayOestR) is stored in OldOestR and updated to thecurrent oestrus date. Finally, StatusR may be set to 2 and days to nextsample may be set to (2×OestOvuIntR+1). It is important to note thatalthough Status has changed to 2 this part of the program (StatusR=2) isnot entered into until the next run.

A following (consecutive) run may come down the “oestrus path” becauseof a following low progesterone sample (LevelR is <LThreshR) or becausethe following run was triggered by an EOD entry (and the cow was foundto not be potentially pregnant). In both of these cases CountR isgreater than 1 i.e. oestrus has already been indicated within thisfollicular period (assuming that SlopeR was greater than SThreshR). Thelength of the follicular phase is updated by adding the interval fromthe last to the current run to FolPerR. At the same time, the length ofthe following luteal period (LutPerR) may be updated to be zero andDayoestR is adjusted to Date—OestOvuIntR. In the luteal phase LutPerRwill be incremented by IntR. Following oestrus, the next sample is taken2×OestOvuIntR.

If the EOD is above EThreshR then it might be worth updating theTimetoAI calculation. In this case no need to revise the previous daysto next sampling value is perceived. If the reason for being in thispart of the model is because of a longer follicular period i.e. EOD<EThreshR, then the risk of a prolonged follicular phase (RiskFolR) andthe days to next sampling (DNSR) are calculated on the basis of thelength of the follicular period. The longer FolPerR, the shorter theinterval to next sampling.

The Luteal Phase (High Progesterone)

This part of the model controls the sampling frequency in the lutealphase and calculates the risk of a prolonged luteal phase i.e., a lutealcyst. This part of the model first deals with a potentialcontra-indication, namely a high EOD during the luteal phase. If thelatest progesterone value is high (above LThreshR) and the current runis triggered by a high EOD (above EThreshR) then something doesn'tmatch. This is resolved by setting Days to Next sample to 0 to verifythe progesterone level. Implicit in this is the assumption that a truehigh EOD cannot occur if progesterone is high.

The length of the luteal phase is accumulated by the time since theprevious run if LevelR is above LThreshR. The Risk of a luteal cyst(RiskLutR) is calculated on the basis of the length of the currentluteal phase relative to that expected from CyclenR. RLutLagR is aconstant for the average length of the follicular phase. The findingthat retained placenta predisposes for luteal cysts should beincorporated as a multiplier. In the luteal phase, Days to Next Sample(DNSR) is calculated from Cyclen and the slope of progesterone (SlopeR)such that increasing days since last oestrus and a more negative slopeboth decrease the DNSR. The days to next sample decreases the longer theinterval since last oestrus. If the system detects a decline inprogesterone i.e., SlopeR is negative, then DNSR is reduced. The morenegative the slope the shorter the DNSR.

StatusR=2 Potentially regnant (FIG. 7 d)

The change to StatusR=2 could be triggered in 2 ways; because an oestrusis detected or because an insemination has been recorded. Making Status2 a consequence of a detected oestrus means that the cow is assumed tobe potentially pregnant prior to any information about an insemination.The reason for this relates back to the underlying premise thatprogesterone is the definitive measure of reproductive status. In otherwords, this way of initiating Status 2 ensures that all pregnanciesstart from an oestrus. However, it gets a bit messy if the subsequentmodel run is triggered by information other than a progesterone valuesuch as EOD information within the oestrus period.

Making Status 2 conditional on the input of an insemination record meansthat the cow is considered to be in Status 1 until the inseminationrecord has been entered, this makes accurate and timely inseminationdata input rather important. It requires a rather unsatisfying procedurefor checking the validity of the insemination with respect toprogesterone levels (which may be outdated by the time the AI is input).

Once StatusR=2, the likelihood of the cow being pregnant (LikePregR) iscalculated on the basis of a timely AI (AITimeR), the progesterone levelmeasured after the AI (LevTimeR), and any pregnancy determinations(PDR).

AITimeR describes the effect of mistiming insemination relative tooestrus where inseminations which are too early or too late are lesslikely to result in pregnancy. LevTimeR uses the progesterone values andthe days since AI to indicate the likelihood that there is a pregnancy.This is a bit more complicated. Consider a given time after inseminationeg. 5 days, at this time a progesterone level of 6 ng/ml would beconsidered a good sign that pregnancy had not failed. However, at 21days after insemination 6 ng/ml would be considered a strong sign thatpregnancy was failing. Therefore, it is necessary to make therelationship between progesterone level and likelihood of pregnancy afunction of days since insemination.

As mentioned above, making the change to Status=2 a function of oestrusdetection means that a likelihood of pregnancy may be calculated beforean insemination data has been entered. This will cause the model torevert back to Status 1. In the case where the run is caused by a newprogesterone sample this outcome is fine because the next progesteronesample is set to be somewhere around 5 days after the start of oestrusby which time if there has been no insemination then the cow is notpregnant. However, if the run is caused by something other thanprogesterone e.g., an EOD value, then there are no grounds for judgingthe cow not pregnant. This is dealt with by the use of a variable calledWaitR which is initially set to be the date at which the nextprogesterone sample is due after oestrus is declared. In Status=2, ifthe current RunTime is less than WaitR then the model doesn't run theStatus 2 calculations. WaitR is modified by the input of an inseminationsuch that the status 2 calculations won't be run until probably 5 daysafter the insemination.

Pregnancy determination (PDR) is also included in the calculation oflikelihood of pregnancy. At first sight it seems that a positive PDR isthe most valuable but for likelihood of pregnancy it is actually thenegative PDRs which are interesting ie. the cow is not pregnant. The PDRinput is 0.1 for not pregnant, 0.5 for uncertain, and 1 for definitelypregnant. The default PDR could be 1, the cow is assumed to be pregnantin the absence of any contrary information. Thus, inputing a PDR=0.1will probably cause the likelihood of pregnancy to be so low as to causethe cow to revert to Status 1. In other words, the PDR input allows thefarmer to change the cows status back to cycling if he has good evidencethat the cow is not pregnant.

Once the likelihood of pregnancy (LikePregR) has been calculated this isused to decide whether the cow should stay in Status 2 or revert toStatus 1. If LikePregR is lower than a given threshold (PThreshR) thenthe cow is assumed to not be pregnant and the model loops back throughStatus 1 within the present model run. This is done in real time becauseat the point where it is first possible to definitively classify the cowas not pregnant (approx. 21 days post-insemination) the cow may alreadybe showing her next oestrus.

If the cow is considered to still be potentially pregnant (StatusR=2)then days to next sample (DNSR) is calculated.

List of Inputs and Outputs

Suggested Inputs

Parameter Comment CowID Unique ID, duplicates not allowed RunTimeR Thecurrent time at which the model is activated. Clock synchrony issue hereProgTimeR Date and time of the latest Progesterone sample LevelRConcentration of Progesterone - smoothed output of SSM SlopeR rate ofchange in Progesterone - smoothed output of SSM IntR Time intervalbetween the latest and the previous Progesterone samples - not alwaysnecessary because of keeping LastRunR as a recurrent. EODtimeR Date andtime of and External Oestrus Detection EODinR Likelihood of oestrusbased on external oestrus detection - a value between 0 and 1 EODtypeRThe type of EOD system being used. Controls the accuracy of EODR andEODtimeR PrecMultR Precision Multiplier for reproduction DFC Days fromCalving - should be validated against prior pregnancy and AI records.Breed Code for different breeds including “other” Parity 1, 2, or 3+ProbTypeR Code for type of reproductive problem ProbDateR Date of firstidentification ProbSevR Code for severity of problem: mild = 0.3,average = 0.6, severe = 0.9 ProbCertR Code for certainty of diagnosis:possible = 0.3, probable = 0.6, definite = 0.9 EnStat Energy status,output of another model AIdateR Date of latest insemination PDdateR Dateof latest pregnancy determination PDR Code for outcome of pregnancydetermination: Not pregnant = 0.1, Uncertain = 0.5, Pregnant = 1.0

Suggested Outputs to the End-User:

Parameter End-User Comment DNSR Merkur Days to next sampling DayOest1RHerd Man. needed for herd stats ProAnRiskR Cow Man. Risk of ProlongedAnoestrus (cow level) Oestrus Cow Man. indicate cows in oestrusInseminate Cow Man. suggested optimum time for AI (given oestrus)LikeAISuccR Cow Man. Estimated likelihood of success if AIing RiskLutRCow Man. Risk of a luteal cyst RiskFolR Cow Man. Risk of a follicle cystLikePregR Cow Man. Likelihood of pregnancy (given AI) NotPregFlagR Cow.Man Flag changing back to Status 1 if more than 30 days post AIMax(OestN) Herd Man. needs Developer specifying

Application of the Present Invention for the Determination of Urea andProtein Status

In yet a preferred embodiment of the present invention urea and proteinstatus is detected in animals, as shown in FIG. 8. The informationprovided by milk urea may be difficult to interpret withoutsimultaneously considering energy status and having feed formulationinformation. The availability of this information will allow individualanimal feeding.

Furthermore, as illustrated in FIG. 8, the biological model for urea isvery simple. The reason for this is that there can be very littlebiological interpretation of urea data unless there is an associatedevaluation of energy status. Furthermore, abnormal milk urea levels maybe due to both problems in the balance of feed protein sources (proteinquality) and imbalances in the ratio of protein to energy.

The purpose of the urea model presented here is to allow monitoring ofmilk urea levels on a group basis (monitoring on individual basis isalso possible). This is expected to be useful relative to milk qualityand environmental pollution regulatory requirements. The model consistsof a biometric component to obtain smoothed individual cow urea valuesfrom which group averages are obtained.

Note: All the urea model parameter names end with a capital U (forUrea), drop the ending ‘U’ and they should be (to some degree) readable.

Group Definition

This should come from a Herd Management System i.e. farmer input. Thefarmer needs to input the relevant “physical group” for each cow. The“physical group” could be one or more of:

-   Physical location: the shed or subdivision of shed within which the    cow is located.-   Milking group: this would usually be the same as physical location.-   Feeding group: this may not be the same as physical location if    advanced feed distribution systems are present.

It is also envisaged that it should be possible to present resultsaccording to “biological group” i.e, groupings based on breed, parityand stage of lactation.

Urea Threshold Display

Given the biological group information and information from theliterature a default optimum urea level for each biological group can beincluded in the model from which default upper and lower recommendedurea levels will be generated for use as reference levels. These shouldbe modifiable by the user to account for local conditions such as feedtype and regulatory requirements.

Triggering a Run of the Model and Handling Sampling Frequency

Days to next sample is directly under the control of the user throughthe input of Tracking Degree. In addition, the user is also required tospecify what proportion of each of the groups that requires or needs tobe sampled. In this way it is possible to tailor the precision of theurea measurements to match the interest in milk urea levels.

Presentation of Group Urea Results

The end-user should be able see the progression of group urea levelsrelative to both calendar date and days from calving (DFC). Thus,calculation of average (AvgLevelU) and standard deviation (StdLevelU) ofurea level should be done for each DFC or each calendar date. It couldalso be useful to characterise which type of cow ie., biological group,is dominant in the tails of the distribution of urea levels. It willalso require that the cows chosen for urea sampling are evenlydistributed across the relevant biological groups.

List of Inputs and Outputs

Suggested Inputs

Parameter Comment CowID Unique ID, duplicates not allowed UreaTimeU Dateand time of the latest Urea sample LevelU Concentration of Urea -smoothed output of SSM PrecMultU no of sample/group DNSU tracking degreeGroup physical group definition DFC Days from Calving - should bevalidated against prior pregnancy and AI records. Breed Code fordifferent breeds including “other” Parity 1, 2, or 3+ UpThreshURecommended upper urea level LoThreshU Recommended lower urea level

Suggested Outputs to the End-User:

Parameter Comment GroupTime the days from calving, or calendar date, towhich the average urea etc relate AvgLevelU Group Average Urea StdLevelUGroup standard deviation Urea

Application of the Present Invention for the Determination of Milk Fat,Milk Protein, and Energy Status

In another preferred embodiment of the present invention milk fat, milkprotein, and energy status is detected in animals, as shown in FIG. 9.Energy status is defined as the size of the body lipid (energy)mobilisation which is in excess of that mobilisation which is totallynatural for a cow in a non compromising situation. This is depicted inthe graph, the energy status equation, correspond to the hatchedcomponents of the graph. It is anticipated that the non-compromised rateof body mobilisation can be derived with reasonable accuracy frominformation about genotype, parity and days from calving. However,measuring total body loss (dBL/dt) in real time is a rather moredaunting prospect. Milk fat: protein ratio should provide an indicatorespecially when used in conjunction with urea measurements.

An alternative model could be established for milk fat and protein.

The cost per sample of measuring a parameter is normally relatively highand the models discribed therefore needs to be able to run in a stableway when the frequency of sampling is very low. At low frequencies theperformance of the state space model may be substantially reduced.Therefore, the number of parameters and external parameters included inthe model may be variable which will e.g. be dependent on the nationalrequirements regarding accuracy and precision of the measurement.

The described non-limiting models of how to detect a selected parametershould not be limited to the described models as it is obvious to theperson skilled in the art how to modify and optimise the models.Furthermore, the scope of the present invention is not limited to theparameters (BHB, Mastitis Indicator (e.g, LDH or NAGase), progesterone,urea, protein status, milk fat, milk protein and energy status) whichhas been additionally explained above.

EXAMPLES

In order to illustrate the methodology, data {y_(t)}_(t=1, . . . , 50)were generated according to the linear growth model with exceptions,conditional on M_(t)(j), given by observation equation:Y_(t)=F_(t)θ_(t)+v_(t), with F_(t)=(1 0) and θ_(t)=(μ_(t),β_(t))′;system equation(s) μ_(t)=μ_(t-1)+β_(t)+ε_(μt) and β_(t)=β_(t-1)+ε_(βt)with v_(t)|M_(t)(j)˜N(0,V(j)), ε_(μt)|M_(t)(j)˜N(0,E_(μ)(j)) andε_(βt)|M_(t)(j)˜N(0,E_(β)(j)), j=1, . . . , 4 and t=1, . . . , 50assumed to be mutually independent.

In FIG. 3, chart A depicts simulated data, chart B depicts posteriorprobabilities of the 4 different models at time t=1, . . . , 50, andcharts C and D depict one and two step back smoothed probabilities ofthe 4 different models at time t=1, . . . , 49 and time t=1, . . . , 48,respectively.

$\left. {{{\left. {{\left( {\theta_{t} = \begin{pmatrix}1 & 1 \\0 & 1\end{pmatrix}}\quad \right.\theta_{t - 1}} + {w_{t}\mspace{14mu}{with}\mspace{14mu} w_{t}}} \middle| {{M_{t}(j)} \sim {N_{2}\left( {0,{W_{t}(j)}} \right)}} \right.;}\quad}{{W_{t}(j)} = \begin{pmatrix}{{E_{\mu}(j)} + {E_{\beta}(j)}} & {E_{\mu}(j)} \\{E_{\mu}(j)} & {E_{\mu}(j)}\end{pmatrix}}} \right)$

Values of the different parameters are given in Table 1. θ₀ wasarbitrary set to (4 0)′ and all of the data were simulated from Model 1(steady state) except for a change in level at time 10, a change inslope at time 25 and an outlier at time 40. The parameters from Table 1were used in the analysis and the initial information was (arbitrary)assumed to be

$\theta_{0} \sim {{N_{2}\left( {\begin{pmatrix}4 \\0\end{pmatrix},\begin{pmatrix}20 & 0 \\0 & 10\end{pmatrix}} \right)}.}$

TABLE 1 Parameters used for analysing simulated data. Model j Name π₀(j) V (j) E_(μ) (j) E_(β) (j) 1 steady state 0.94 1.0 0.0 0.0 2 chancein level 0.02 1.0 20.0 0.0 3 change in slope 0.02 1.0 0.0 10.0 4 outlier0.02 50.0 0.0 0.0

The simulated data are shown in FIG. 3.A. Posterior probabilities (FIG.3.B) of the outlier model are very high at times 10, 25 and 40. I.e.abrupt changes are detected—but not the true nature of the changes. Theoutlier is pointed out (wihtout false positive detections of outliers)from one step back smoothed probabilities (FIG. 3.C). Finally from twostep back smoothed probabilites (FIG. 3.D) high probabilities of themodels used in the simulation are obtained at all timepoints.

There is thus provided modelling and monitoring of biological timeseries subject to outliers and changes in the underlying latentvariables presented in Smith and West (1983). Smith and West used themethod successfully to provide on-line probabilities of serious changesin kidney function in individual patients who had recently receivedtransplants. However, it has been found that the method is relevant inagriculture. For example, based on regular measurement of ln(somaticcell count), or other indicators of mastitis, it is possible to provideprobabilities of mastitis and to detect mastitis earlier and morereliably than with other methods due to the flexibility of the models.In applications of multiprocess II mixture models, parameters have beenfound from empirical trials with the system (see e.g. Smith and West(1983) and Thysen (1992)). Moreover, information from relatives may beincorporated in mixture models, and breeding value estimation may beintegrated.

Though, in the above discussion, cows have been mentioned as an exampleof animals, it should be understood that the discussed principles andfeatures apply equally well to other especially domestic animals,including sheep, goat, buffalo, camel, pig, horse, and chicken.

REFERENCES

-   Harrison P. J., Stevens C. F. (1976) J. of the Stat. Soc. Ser. B 38,    205-247.-   Smith A. F. M., West M. (1983) Biometrics 39, 867-878.-   Thysen I. (1993) Acta Agric. Scand. Sect. A., Animal Sci. 43, 58-64.-   U.S. Pat. No. 6,405,672-   R. M. de Mol (2000), “Automated detection of oestrus and mastitis in    dairy cows”.

1. A system for observing and predicting a physiological state of ananimal, the system comprising: a computer comprising a processor andbeing operatively connected to a database, at least one sample providingdevice for repetitively providing at least one sample of a body fluid ofthe animal, an analysis apparatus for analysing the at least one sample,so as to obtain at least one sample value of each of a plurality ofparameters of the body fluid, a data interface for repetitively enteringthe sample value of each of the parameters in the database, wherein thedatabase is adapted to store multiple database entries representing thesample value of each of the parameters at various points in time, andwherein the processor is programmed to: perform a univariate analysis ofthe database entries to obtain a first set of data representing expectedsample values of at least one of the parameters at future points intime, perform a multivariate analysis of the database entries to producea second set of data derived from combined analysis of sample values ofat least two of the parameters, combine the first and second sets ofdata to obtain a third set of data representative of the physiologicalstate of the animal, store the first, second and third sets of data inthe database, and compare the third set of data with a pattern inmeasured parameters in order to observe and predict the physiologicalstate of an animal.
 2. A system according to claim 1, wherein theprocessor is programmed to perform the univariate analysis employing atleast one State Space Model (SSM).
 3. A system according to claim 1,wherein the processor is programmed to perform the multivariateprojection analysis, such as Principal Component Analysis (PCA).
 4. Asystem according to claim 1, wherein the at least one sample of a bodyfluid comprises at least one raw milk sample, and wherein the at leastone sample providing device is arranged to provide the raw milk sample.5. A system according to claim 1, wherein the analysis apparatus isarranged to perform spectroscopic analysis, such as analysis in a near-or mid-infrared spectrum, of the sample of body fluid.
 6. A systemaccording to claim 1, wherein the analysis apparatus comprises solidsupport analytical devices.
 7. A system according to claim 1, whereinthe analysis apparatus is arranged to determine the value(s) of at leastone of: Nagase, Progesterone, milk yield, FPD and Conductivity, Fat A,Fat B, Protein, Lactose, Urea, Citric Acid, LDH, TS, SNF, and one ormore of the ketone bodies in the one or more samples.
 8. A systemaccording to claim 1, wherein the database stores at least onepredetermined set of data representing at least one physiological stateof the animal, and wherein the processor is further programmed tocompare the third set of data to the at least one predetermined set ofdata.
 9. A system according to claim 1, wherein physiological state ofthe animal is observed or predicted from comparing the third set of datawith a reference pattern which is typical for healthy animals and apattern which is typical for animals suffering from a certain disease.10. A method for observing and predicting a physiological state of ananimal, the method comprising: repetitively providing at least onesample of a body fluid of the animal, analysing the at least one sample,so as to obtain at least one sample value of each of a plurality ofparameters of the body fluid, entering the sample value of each of theparameters in a database of a computer system, whereby the database isloaded with multiple database entries representing the sample value ofeach of the parameters at various points in time, and performing aunivariate analysis of the database entries to obtain a first set ofdata representing expected values of at least one of the parameters atfuture points in time, performing a multivariate analysis of thedatabase entries to produce a second set of data derived from combinedanalysis of sample values of at least two of the parameters, combiningthe first and second sets of data to obtain a third set of datarepresentative of the physiological state of the animal, storing thefirst, second and third sets of data in the database, and comparing thethird set of data with a pattern in measured parameters in order toobserve and predict the physiological state of the animal.
 11. A methodaccording to claim 10, wherein the univariate analysis is performed inat least one State Space Model (SSM).
 12. A method according to claim11, wherein the State Space Model provides probabilities of changes. 13.A method according to claim 12, wherein a dynamic linear model isextended to a multiprocess class II mixture model with a recursiveupdating procedure.
 14. A method according to claim 10, wherein themultivariate analysis is performed as a multivariate projectionanalysis, such as Principal Component Analysis (PCA).
 15. A methodaccording to claim 14, wherein, in the multivariate analysis, at leasttwo disjoint sets of Principal Components are selected.
 16. A methodaccording to claim 14, wherein historical sample values are grouped inclasses, and wherein the classes are analysed individually by means ofPrincipal Component Analysis.
 17. A method according to claim 10,wherein the at least one sample of a body fluid comprises at least oneraw milk sample, and wherein the at least one sample providing deviceprovides the raw milk sample.
 18. A method according to claim 10,comprising, at the step of analysing the at least one sample, performingspectroscopic analysis of the sample of body fluid.
 19. A methodaccording to claim 18, wherein the processor is programmed to performthe multivariate data analysis using a PLSR model.
 20. A methodaccording to claim 18, wherein the spectroscopic analysis is performedat near- or mid-range infrared spectrum.
 21. A method according to claim10, wherein the physiological state is determined from a comparison of apattern in the sample values and a pattern of reference parameter valueswhich is typical for a certain predetermined physiological state.
 22. Amethod according to claim 21, wherein a mathematical model of the formX=T _(a) *P _(a) ′+E is used in the multivariate data analysis, whereinX represents the scaled or otherwise pre-processed matrix of the samplevalues, T represents latent variables, P is the eigenvector of acorrelation matrix determined as X′X, and E is a residual matrix whichcollects random noise, and a denotes a dimension of the model.
 23. Amethod according to claim 22, wherein sample values are sampled in apattern x_(i), and wherein the projection of x_(i) on each of themodels, t_(i), is determined as t_(i)=x_(i)*P.
 24. A method according toclaim 23, wherein: a leverage h, is determined as the square sum of theelements of t_(i), a residual r is determined as the square sum of theelements in a vector determined as x_(i)−t_(i)*P′, the quantities h andr are normalised with their respective 95% significance levels for themathematical model, and wherein x_(i) is chosen to belong to the modelin question if the length of vector (h,r) is less than √2.
 25. A methodaccording to claim 10, wherein the value(s) of at least one or more of:Nagase, Progesterone, milk yield, FPD and Conductivity, Fat A, Fat B,Protein, Lactose, Urea, Citric Acid, TS, SNF, and one or more of theketone bodies is/are determined in the analysis apparatus.
 26. A methodaccording to claim 10, wherein the database entries further comprise atleast one external value of at least one external parameter, and whereinthe multivariate data analysis employs the at least one external value.27. A method according to claim 26, wherein the at least one externalparameter comprises at least one of: the age of the animal, the breed orrace of the animal, the weight of the animal, the reproduction of theanimal, feeding particulars, season, and geographical location,identification to the herd of origin.
 28. A method according to claim10, wherein the database stores at least one predetermined set of datarepresenting at least one physiological state of the animal, and whereinthe processor is further programmed to compare the third set of data tothe at least one predetermined set of data.
 29. A method according toclaim 10, wherein physiological state of the animal is observed orpredicted from comparing the third set of data with a reference patternwhich is typical for healthy animals and a pattern which is typical foranimals suffering from a certain disease.
 30. A method for observing andpredicting a physiological state of an animal, the method comprising:repetitively providing at least one sample of a body fluid of theanimal, analysing the at least one sample, so as to obtain at least onesample value of each of a plurality of parameters of the body fluid,entering the sample value of each of the parameters in a database of acomputer system, whereby the database is loaded with multiple databaseentries representing the sample value of each of the parameters atvarious points in time, and performing State Space Model (SSM) analysisof the database entries to obtain data representative of thephysiological state of the animal, comparing the data from the StateSpace Model (SSM) analysis with a pattern in measured parameters inorder to observe and predict the physiological state of the animal. 31.A method according to claim 30, wherein physiological state of theanimal is observed or predicted from comparing the data from the StateSpace Model (SSM) analysis with a reference pattern which is typical forhealthy animals and a pattern which is typical for animals sufferingfrom a certain disease.
 32. A method for observing and predicting aphysiological state of an animal, the method comprising: repetitivelyproviding at least one sample of a body fluid of the animal, analysingthe at least one sample, so as to obtain at least one sample value ofeach of a plurality of parameters of the body fluid, entering the samplevalue of each of the parameters in a database of a computer system,whereby the database is loaded with multiple database entriesrepresenting the sample value of each of the parameters at variouspoints in time, performing at least one of a Principal ComponentAnalysis and a Partial Least Squares Regression of the database entriesto obtain data representative of the physiological state of the animal,and comparing the data from the at least one of a Principal ComponentAnalysis and a Partial Least Squares Regression analysis with a patternin measured parameters in order to observe and predict the physiologicalstate of the animal.
 33. A method according to claim 32, whereinphysiological state of the animal is observed or predicted fromcomparing the data from the at least one of a Principal ComponentAnalysis and a Partial Least Squares Regression analysis with areference pattern which is typical for healthy animals and a patternwhich is typical for animals suffering from a certain disease.